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Articles Tagged Aging Curves 

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May 24, 2013 5:00 am

Pebble Hunting: How Pitchers Age at the Plate

4

Sam Miller

When do pitchers reach their offensive peak?

You’ll notice that Tim Lincecum isn’t very good these days. He’s actually quite bad. It’s hard to watch him sometimes, especially when he’s matched up against a good opponent, knowing he’s become so likely to lose the battle. That’s what happens, I suppose: pitchers get older, they get worse.

Not his pitching, though. I’m talking about his hitting. Lincecum has one measly hit this season, a little groundball single through the hole between shortstop and third base. He has struck out 12 times in 16 official at-bats. Just six players—five pitchers, and Khris Davis, who R.J. brilliantly describes as “Chris Davis with more K”—have a lower contact rate on pitches in the strike zone.

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February 22, 2010 12:01 pm

Ahead in the Count: Evaluating Multi-Year Deals

29

Matt Swartz

A surprising revelation that players often do better in the second year of two-year contracts than the first.

Each year, about 25 players receive two-year contracts. The inevitable question that analysts ask is whether it was smart to commit to the player for a second year, or whether the team should have stuck with one year. But did you know that most players receiving two-year deals in recent years actually do better in the second year of their contract? Players who receive three- and four-year deals produce similarly in the first two years of their deals as well, instead of declining as many people believe.

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February 22, 2007 12:00 am

Schrodinger's Bat: Turning the Page

0

Dan Fox

Dan hits on a few topics related to age and team success, including a more detailed look at the NL Central.

"Age is a case of mind over matter. If you don't mind, it don't matter."
--Satchel Paige

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Reasonable Person Standard

I got BP rolling many years ago in large part because of a forecasting system I had created called Vladimir. Vlad was basically a two-step system. The first step was categorization: What type of player is this? What is the shape of his performance? Is he a slow masher? A waterbug? A power-and-speed guy? How old is he? The second step was a neural-net system, which basically "walked" the player in question down their expected career path. I used Clay Davenport's DTs as the inputs for the system, because it helped me out in terms of removing park and league effects.

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